{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Using information theory to evaluate features"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The mutual information (MI) between a feature and the outcome is a measure of the mutual dependence between the two variables. It extends the notion of correlation to nonlinear relationships. More specifically, it quantifies the information obtained about one random variable through the other random variable.\n",
    "\n",
    "The concept of MI is closely related to the fundamental notion of entropy of a random variable. Entropy quantifies the amount of information contained in a random variable. Formally, the mutual information—I(X, Y)—of two random variables, X and Y, is defined as the following:\n",
    "\n",
    "The sklearn function implements feature_selection.mutual_info_regression that computes the mutual information between all features and a continuous outcome to select the features that are most likely to contain predictive information. There is also a classification version (see the documentation for more details). \n",
    "\n",
    "This notebook contains an application to the financial data we created in Chapter 4, Alpha Factor Research."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import warnings\n",
    "warnings.filterwarnings('ignore')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-17T17:16:51.261274Z",
     "start_time": "2020-06-17T17:16:50.052272Z"
    }
   },
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "\n",
    "import pandas as pd\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "\n",
    "from sklearn.feature_selection import mutual_info_classif"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-17T17:16:51.266981Z",
     "start_time": "2020-06-17T17:16:51.263326Z"
    }
   },
   "outputs": [],
   "source": [
    "sns.set_style('whitegrid')\n",
    "idx = pd.IndexSlice"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Get Data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We use the data produced in [Chapter 4](../../04_alpha_factor_research/00_data/feature_engineering.ipynb).\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-17T17:16:51.781719Z",
     "start_time": "2020-06-17T17:16:51.270749Z"
    }
   },
   "outputs": [],
   "source": [
    "with pd.HDFStore('../data/assets.h5') as store:\n",
    "    data = store['engineered_features']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Create Dummy variables"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-17T17:16:52.354197Z",
     "start_time": "2020-06-17T17:16:51.783915Z"
    },
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "MultiIndex: 358914 entries, ('A', Timestamp('2001-01-31 00:00:00')) to ('ZUMZ', Timestamp('2018-02-28 00:00:00'))\n",
      "Data columns (total 88 columns):\n",
      " #   Column                 Non-Null Count   Dtype  \n",
      "---  ------                 --------------   -----  \n",
      " 0   return_1m              358914 non-null  float64\n",
      " 1   return_2m              358914 non-null  float64\n",
      " 2   return_3m              358914 non-null  float64\n",
      " 3   return_6m              358914 non-null  float64\n",
      " 4   return_9m              358914 non-null  float64\n",
      " 5   return_12m             358914 non-null  float64\n",
      " 6   Mkt-RF                 358914 non-null  float64\n",
      " 7   SMB                    358914 non-null  float64\n",
      " 8   HML                    358914 non-null  float64\n",
      " 9   RMW                    358914 non-null  float64\n",
      " 10  CMA                    358914 non-null  float64\n",
      " 11  momentum_2             358914 non-null  float64\n",
      " 12  momentum_3             358914 non-null  float64\n",
      " 13  momentum_6             358914 non-null  float64\n",
      " 14  momentum_9             358914 non-null  float64\n",
      " 15  momentum_12            358914 non-null  float64\n",
      " 16  momentum_3_12          358914 non-null  float64\n",
      " 17  return_1m_t-1          357076 non-null  float64\n",
      " 18  return_1m_t-2          355238 non-null  float64\n",
      " 19  return_1m_t-3          353400 non-null  float64\n",
      " 20  return_1m_t-4          351562 non-null  float64\n",
      " 21  return_1m_t-5          349724 non-null  float64\n",
      " 22  return_1m_t-6          347886 non-null  float64\n",
      " 23  target_1m              358914 non-null  float64\n",
      " 24  target_2m              357076 non-null  float64\n",
      " 25  target_3m              355238 non-null  float64\n",
      " 26  target_6m              349724 non-null  float64\n",
      " 27  target_12m             338696 non-null  float64\n",
      " 28  year_2001              358914 non-null  uint8  \n",
      " 29  year_2002              358914 non-null  uint8  \n",
      " 30  year_2003              358914 non-null  uint8  \n",
      " 31  year_2004              358914 non-null  uint8  \n",
      " 32  year_2005              358914 non-null  uint8  \n",
      " 33  year_2006              358914 non-null  uint8  \n",
      " 34  year_2007              358914 non-null  uint8  \n",
      " 35  year_2008              358914 non-null  uint8  \n",
      " 36  year_2009              358914 non-null  uint8  \n",
      " 37  year_2010              358914 non-null  uint8  \n",
      " 38  year_2011              358914 non-null  uint8  \n",
      " 39  year_2012              358914 non-null  uint8  \n",
      " 40  year_2013              358914 non-null  uint8  \n",
      " 41  year_2014              358914 non-null  uint8  \n",
      " 42  year_2015              358914 non-null  uint8  \n",
      " 43  year_2016              358914 non-null  uint8  \n",
      " 44  year_2017              358914 non-null  uint8  \n",
      " 45  year_2018              358914 non-null  uint8  \n",
      " 46  month_1                358914 non-null  uint8  \n",
      " 47  month_2                358914 non-null  uint8  \n",
      " 48  month_3                358914 non-null  uint8  \n",
      " 49  month_4                358914 non-null  uint8  \n",
      " 50  month_5                358914 non-null  uint8  \n",
      " 51  month_6                358914 non-null  uint8  \n",
      " 52  month_7                358914 non-null  uint8  \n",
      " 53  month_8                358914 non-null  uint8  \n",
      " 54  month_9                358914 non-null  uint8  \n",
      " 55  month_10               358914 non-null  uint8  \n",
      " 56  month_11               358914 non-null  uint8  \n",
      " 57  month_12               358914 non-null  uint8  \n",
      " 58  msize_-1               358914 non-null  uint8  \n",
      " 59  msize_1                358914 non-null  uint8  \n",
      " 60  msize_2                358914 non-null  uint8  \n",
      " 61  msize_3                358914 non-null  uint8  \n",
      " 62  msize_4                358914 non-null  uint8  \n",
      " 63  msize_5                358914 non-null  uint8  \n",
      " 64  msize_6                358914 non-null  uint8  \n",
      " 65  msize_7                358914 non-null  uint8  \n",
      " 66  msize_8                358914 non-null  uint8  \n",
      " 67  msize_9                358914 non-null  uint8  \n",
      " 68  msize_10               358914 non-null  uint8  \n",
      " 69  age_0                  358914 non-null  uint8  \n",
      " 70  age_1                  358914 non-null  uint8  \n",
      " 71  age_2                  358914 non-null  uint8  \n",
      " 72  age_3                  358914 non-null  uint8  \n",
      " 73  age_4                  358914 non-null  uint8  \n",
      " 74  age_5                  358914 non-null  uint8  \n",
      " 75  Basic Industries       358914 non-null  uint8  \n",
      " 76  Capital Goods          358914 non-null  uint8  \n",
      " 77  Consumer Durables      358914 non-null  uint8  \n",
      " 78  Consumer Non-Durables  358914 non-null  uint8  \n",
      " 79  Consumer Services      358914 non-null  uint8  \n",
      " 80  Energy                 358914 non-null  uint8  \n",
      " 81  Finance                358914 non-null  uint8  \n",
      " 82  Health Care            358914 non-null  uint8  \n",
      " 83  Miscellaneous          358914 non-null  uint8  \n",
      " 84  Public Utilities       358914 non-null  uint8  \n",
      " 85  Technology             358914 non-null  uint8  \n",
      " 86  Transportation         358914 non-null  uint8  \n",
      " 87  Unknown                358914 non-null  uint8  \n",
      "dtypes: float64(28), uint8(60)\n",
      "memory usage: 98.6+ MB\n"
     ]
    }
   ],
   "source": [
    "dummy_data = pd.get_dummies(data,\n",
    "                            columns=['year','month', 'msize', 'age',  'sector'],\n",
    "                            prefix=['year','month', 'msize', 'age', ''],\n",
    "                            prefix_sep=['_', '_', '_', '_', ''])\n",
    "dummy_data = dummy_data.rename(columns={c:c.replace('.0', '') for c in dummy_data.columns})\n",
    "dummy_data.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Mutual Information"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Original Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-17T17:16:52.627157Z",
     "start_time": "2020-06-17T17:16:52.355745Z"
    }
   },
   "outputs": [],
   "source": [
    "target_labels = [f'target_{i}m' for i in [1,2,3,6,12]]\n",
    "targets = data.dropna().loc[:, target_labels]\n",
    "\n",
    "features = data.dropna().drop(target_labels, axis=1)\n",
    "features.sector = pd.factorize(features.sector)[0]\n",
    "\n",
    "cat_cols = ['year', 'month', 'msize', 'age', 'sector']\n",
    "discrete_features = [features.columns.get_loc(c) for c in cat_cols]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-17T17:32:47.082116Z",
     "start_time": "2020-06-17T17:16:52.632368Z"
    }
   },
   "outputs": [],
   "source": [
    "mutual_info = pd.DataFrame()\n",
    "for label in target_labels:\n",
    "    mi = mutual_info_classif(X=features, \n",
    "                             y=(targets[label]> 0).astype(int),\n",
    "                             discrete_features=discrete_features,\n",
    "                             random_state=42\n",
    "                            )\n",
    "    mutual_info[label] = pd.Series(mi, index=features.columns)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-17T17:32:47.094557Z",
     "start_time": "2020-06-17T17:32:47.083903Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "target_1m     0.031324\n",
       "target_2m     0.060676\n",
       "target_3m     0.090796\n",
       "target_6m     0.138737\n",
       "target_12m    0.200261\n",
       "dtype: float64"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mutual_info.sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Normalized MI Heatmap"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-17T17:32:47.827638Z",
     "start_time": "2020-06-17T17:32:47.099645Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x288 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax= plt.subplots(figsize=(15, 4))\n",
    "sns.heatmap(mutual_info.div(mutual_info.sum()).T, ax=ax, cmap='Blues');"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Dummy Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-17T17:32:48.179953Z",
     "start_time": "2020-06-17T17:32:47.829215Z"
    }
   },
   "outputs": [],
   "source": [
    "target_labels = [f'target_{i}m' for i in [1, 2, 3, 6, 12]]\n",
    "dummy_targets = dummy_data.dropna().loc[:, target_labels]\n",
    "\n",
    "dummy_features = dummy_data.dropna().drop(target_labels, axis=1)\n",
    "cat_cols = [c for c in dummy_features.columns if c not in features.columns]\n",
    "discrete_features = [dummy_features.columns.get_loc(c) for c in cat_cols]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-17T17:49:10.451633Z",
     "start_time": "2020-06-17T17:32:48.181357Z"
    }
   },
   "outputs": [],
   "source": [
    "mutual_info_dummies = pd.DataFrame()\n",
    "for label in target_labels:\n",
    "    mi = mutual_info_classif(X=dummy_features, \n",
    "                             y=(dummy_targets[label]> 0).astype(int),\n",
    "                             discrete_features=discrete_features,\n",
    "                             random_state=42\n",
    "                            )    \n",
    "    mutual_info_dummies[label] = pd.Series(mi, index=dummy_features.columns)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-17T17:49:10.463663Z",
     "start_time": "2020-06-17T17:49:10.452923Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "target_1m     0.032464\n",
       "target_2m     0.062606\n",
       "target_3m     0.093501\n",
       "target_6m     0.142711\n",
       "target_12m    0.204995\n",
       "dtype: float64"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mutual_info_dummies.sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-17T18:00:10.869337Z",
     "start_time": "2020-06-17T18:00:09.078374Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 288x1440 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax= plt.subplots(figsize=(4, 20))\n",
    "sns.heatmap(mutual_info_dummies.div(mutual_info_dummies.sum()), ax=ax, cmap='Blues');"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.7"
  },
  "toc": {
   "base_numbering": 1,
   "nav_menu": {},
   "number_sections": true,
   "sideBar": true,
   "skip_h1_title": false,
   "title_cell": "Table of Contents",
   "title_sidebar": "Contents",
   "toc_cell": false,
   "toc_position": {},
   "toc_section_display": true,
   "toc_window_display": true
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
